Remove distutils, drop support for old sklearn

tudo-astroparticlephysics · Jan 10, 2024 · 4606b8c · 4606b8c
1 parent dfd6e7b
commit 4606b8c
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Showing 3 changed files with 24 additions and 50 deletions.
diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
@@ -18,7 +18,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.9", "3.10", "3.11"]
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
 
     steps:
       - uses: actions/checkout@v4

diff --git a/funfolding/binning/tree_sklearn_based.py b/funfolding/binning/tree_sklearn_based.py
@@ -1,6 +1,5 @@
 import numpy as np
 import copy
-from distutils.version import StrictVersion
 
 import sklearn
 from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
@@ -10,8 +9,6 @@
 import warnings
 
 
-old_sklean = StrictVersion("0.19.0") >= StrictVersion(sklearn.__version__)
-
 
 def __sample_uniform__(y, sample_weight=None, random_state=None):
     """Function used to sample a uniform distribution from a binned y.
@@ -298,28 +295,16 @@ def __init__(self,
             self.uniform = uniform
 
         if regression:
-            if old_sklean:
-                if min_impurity_decrease != 0.:
-                    warnings.warn('min_impurity_decrease is supported '
-                                  ' only in sklearn version >= 0.19.0')
-                self.tree = DecisionTreeRegressor(
-                    max_depth=max_depth,
-                    min_samples_split=min_samples_split,
-                    min_samples_leaf=min_samples_leaf,
-                    max_leaf_nodes=max_leaf_nodes,
-                    max_features=max_features,
-                    min_weight_fraction_leaf=min_weight_fraction_leaf,
-                    random_state=random_state)
-            else:
-                self.tree = DecisionTreeRegressor(
-                    max_depth=max_depth,
-                    min_samples_split=min_samples_split,
-                    min_samples_leaf=min_samples_leaf,
-                    max_leaf_nodes=max_leaf_nodes,
-                    max_features=max_features,
-                    min_impurity_decrease=min_impurity_decrease,
-                    min_weight_fraction_leaf=min_weight_fraction_leaf,
-                    random_state=random_state)
+            self.tree = DecisionTreeRegressor(
+                max_depth=max_depth,
+                min_samples_split=min_samples_split,
+                min_samples_leaf=min_samples_leaf,
+                max_leaf_nodes=max_leaf_nodes,
+                max_features=max_features,
+                min_impurity_decrease=min_impurity_decrease,
+                min_weight_fraction_leaf=min_weight_fraction_leaf,
+                random_state=random_state,
+            )
             if boosted in ['linear', 'square', 'exponential']:
                 self.boosted = AdaBoostRegressor(
                     base_estimator=self.tree,
@@ -336,35 +321,24 @@ def __init__(self,
             else:
                 self.boosted = None
         else:
-            if old_sklean:
-                if min_impurity_decrease != 0.:
-                    warnings.warn('min_impurity_decrease is supported '
-                                  ' only in sklearn version >= 0.19.0')
-                self.tree = DecisionTreeClassifier(
-                    max_depth=max_depth,
-                    min_samples_split=min_samples_split,
-                    min_samples_leaf=min_samples_leaf,
-                    max_leaf_nodes=max_leaf_nodes,
-                    max_features=max_features,
-                    min_weight_fraction_leaf=min_weight_fraction_leaf,
-                    random_state=random_state)
-            else:
-                self.tree = DecisionTreeClassifier(
-                    max_depth=max_depth,
-                    min_samples_split=min_samples_split,
-                    min_samples_leaf=min_samples_leaf,
-                    max_leaf_nodes=max_leaf_nodes,
-                    max_features=max_features,
-                    min_weight_fraction_leaf=min_weight_fraction_leaf,
-                    min_impurity_decrease=min_impurity_decrease,
-                    random_state=random_state)
+            self.tree = DecisionTreeClassifier(
+                max_depth=max_depth,
+                min_samples_split=min_samples_split,
+                min_samples_leaf=min_samples_leaf,
+                max_leaf_nodes=max_leaf_nodes,
+                max_features=max_features,
+                min_weight_fraction_leaf=min_weight_fraction_leaf,
+                min_impurity_decrease=min_impurity_decrease,
+                random_state=random_state,
+            )
             if boosted in ['SAMME', 'SAMME.R']:
                 self.boosted = AdaBoostClassifier(
                     base_estimator=self.tree,
                     n_estimators=n_estimators,
                     learning_rate=learning_rate,
                     algorithm=boosted,
-                    random_state=random_state)
+                    random_state=random_state,
+                )
             elif boosted is not None:
                 raise ValueError(
                     '\'boosted\' should be None for no boosting '

diff --git a/setup.py b/setup.py
@@ -42,7 +42,7 @@
         'matplotlib',
         'numpy',
         'pymc3',
-        'scikit-learn>=0.18.1',
+        'scikit-learn>=0.19.0',
         'scipy',
         'six>=1.1',
     ],